Umut. Dişel

Predicting transcriptional outcomes of novel multigene perturbations with GEARS - Nature Biotechnology nature.com/articles/s4158… 🧬🖥️🧪 github.com/snap-stanford/…

In our large Turkish cohort (n=269), T-DXd delivered median PFS 17.9 mo & OS 35.7 mo in HER2+ mBC – consistent with trials, even in real-world LMIC practice. Great to see such robust activity! 🇹🇷 Link: pmc.ncbi.nlm.nih.gov/articles/PMC12… #OncoTwitter #BCancer @DenizCanGuven1 @tkoraysahin @PTarantinoMD @oncodaily @seydagunduz_md @ahmetknower

Eid Mubarak to all! 🌙 I hope you have a very special day with your family and loved ones. Wishing you all peace and happiness.

Understanding ecDNA (tiny circular DNA) is among important open problems in cancer research. However, it can only be detected using whole genome sequencing - a bottleneck to study it at scale. We present ecPath - a method to detect it directly from histopathology images.🧵

3/n To do this, we applied genome amplification-free single-cell WGS (DLP+) to clinical tumors and experimental models across 4 major cancer types, reading CN distributions across cells for each high-level amplification (HLAMP) region in each sample.

Honored to present at the @ASCO Molecular Tumor Boards / Project ECHO session. Grateful to Dr. @benhopark for his time, insightful comments, and for sharing his passion for precision oncology in such an inspiring way. Countless thanks also to my mentors @DrYukselUrun @UmutDisel

Living cells are fundamentally nonequilibrium systems, meaning they constantly spend energy through seemingly one-way, irreversible processes, such as transcribing DNA into RNA, to keep life going. But how that irreversibility appears in the dynamics of individual genes has been difficult to measure. In a new paper in npj Complexity, SFI Postdoctoral Fellow James Holehouse develops analytic tools to study that question, using the canonical two-state model of gene expression to analyze thousands of mouse genes, revealing an interesting pattern. santafe.edu/news-center/ne…

Online Now: The landscape of structural variation in pediatric cancer dlvr.it/TRYG3m

Şimdiye kadarki en büyük memeli beyin ağı ve işlevsel haritası 👇 Nöromorfik bilgisayarlar organoid seviyesinde yani katman katman çalıştıklarında nasıl bir işleme hızı oluşturacaklar tahmin bile edemezsiniz. Şu an çalıştırdığımız clusterlar yanında oyuncak gibi kalacak. Üstelik çok düşük enerji gerekecek. Gelecek çok hızlı şekilleniyor...

The future of diagnostics may fit in a drop of blood. Scientists developed a liquid biopsy platform that reads epigenetic signals in circulating DNA to detect disease and trace its tissue of origin. Early colorectal cancer detection reached ~92% accuracy in validation cohorts.

Cholangiocarcinoma 2026: status quo, unmet needs and priorities nature.com/articles/s4157… #liverpath

Excited to showcase the science and people behind our GigaTIME paper, recently published in Cell. Paper: aka.ms/gigatime-paper Model: aka.ms/gigatime-model Code: aka.ms/gigatime-code Azure Foundry: labs.ai.azure.com/projects/gigat…

Every time you get a cancer biopsy, the lab makes a tissue slide that costs about $5. It shows the shape of your cells under a microscope, and every cancer patient already has one on file. There’s a much fancier version of that test called multiplex immunofluorescence (basically a protein-level map showing which immune cells are near your tumor and what they’re doing). It costs thousands of dollars per sample, takes specialized equipment most hospitals don’t have, and barely scales. But it’s the kind of data oncologists need to figure out whether immunotherapy will actually work for you. Right now, only about 20 to 40% of cancer patients respond to immunotherapy, and one of the biggest reasons is that doctors can’t easily tell whether a tumor is “hot” (immune cells actively fighting it) or “cold” (immune system ignoring it). Microsoft, Providence Health, and the University of Washington trained an AI to analyze the $5 slide and predict what the expensive test would show across 21 different protein markers. They called it GigaTIME, trained it on 40 million cells in which both the cheap slide and the expensive test coexisted, and then turned it loose on 14,256 real cancer patients across 51 hospitals in 7 US states. The results landed in Cell, one of the most selective journals in biology. The model generated about 300,000 virtual protein maps covering 24 cancer types and 306 subtypes. It found 1,234 real, verified connections between immune cell behavior, genetic mutations, tumor staging, and patient survival that were previously invisible at this scale. When they tested it against a completely separate database of 10,200 cancer patients, the results matched up almost perfectly (0.88 out of 1.0 agreement). Nature Methods named spatial proteomics (mapping where specific proteins sit inside your tissue) its Method of the Year in 2024, and specifically cited GigaTIME in a March 2026 update as a model that “democratizes” this kind of analysis. The full model is open-source on Hugging Face. Any cancer research lab with archived biopsy slides, and most of them have thousands, can now run virtual immune profiling without buying a single piece of new equipment.

Gözlemlenebilir Evren'in tamamı tek bir görüntüde böyle görünüyor.

Still surreal - My first paper in Nat Rev Oncol (IF 82) ✨ Our review on metastatic gastric cancer is now live. Grateful to be part of this incredible team led by my mentor Y. @YJanjigianMD with J. Choo, V. Khachatryan, S. Lonardi, F. @FilippoPietran4 R. Sundar & @KlempnerSam

Perfectionism in oncology is not one-dimensional. High standards may help, but discrepancy-driven self-criticism was associated with greater burnout. Excellence should be preserved without turning it into self-doubt. @DrVilmaPBarcia @Ioanna_Nixon @OpenDoors_Onc @DrYukselUrun

Winship researchers have reported a significant advance in the development of next-generation anticancer therapeutics: a precision "molecular glue" strategy that converts previously undruggable cancer mutations into actionable drug targets. ➡️ brnw.ch/21x0EQc

Ben bilmiyordum. Game of Thrones film müziğinin bestecisi İranlı Ramin Djawadi imiş. Reza Sajjadi yorumuyla Deflerle çalınmış.. Şahane değil mi?